An understanding of semantic categories (i.e., how people represent the meanings of categories expressed by natural language) is central to many issues in cognitive psychology, such is language, memory, problem-solving, and decision making. In this research project, we are developing a model of semantic category representation that extends and enhances the "classical" approach to category representation by including abstract feature-based representations, and that also accounts for data previously thought to be inconsistent with such a model. To do so, we examine people's representations of natural language categories, and the implications of these representations. In the last year, the model has been extended to explain inter-category differences in class inclusion judgments, cross-cultural differences in category membership and typicality, and people's representations of semantic relations.